Methodologies based on data driven models are commonly formed by two building blocks, i.e. a time-series data representation block and a regression or classification block. The time-series data representation block is usually formed by two processing steps, i.e. feature extraction and feature learning. In the feature extraction step the raw time-series data is pre-processed and transformed into feature vectors. Handcrafted features encoding the statistics of the time-series at a short-term and at a long-term window are extracted from multi-dimensional time-series in [1]}. Handcrafted features based on technical and quantitative analysis and time-insensitive and time-sensitive indicators are proposed in [2]}, while an extensive list of econometric features is proposed in [3]}. Using such time-series representations, feature learning can be conducted by using subspace learning techniques, like Principal Component Analysis [4]}, Linear Discriminant Analysis [5]}, or Auto-Encoder networks [6]}. A data-driven time-series representation scheme jointly optimizing the feature representation step based on soft vector quantization and the feature learning step based on Clustering-based Discriminant Analysis was proposed in [7]}. Handcrafted time-series representations like those described above are combined with standard classification models. The Support Vector Machine classifier was used in [8]}, [9]}, [1]}, while in [11]} a Support Vector Machine classifier with adaptive parameters was shown to perform better than the standard one. A Multilayer Perceptron was used in [8]} to predict future bonds returns, while in [13]}, a Heterogeneous Multilayer Generalized Operational Perceptron was shown to build high-performing compact neural network topologies.
